Jason Lowe commented on HADOOP-15206:

Thanks for updating the patch!

bq. In the current implementation, read only "BZ" header when the read mode is 
CONTINUOUS. Do you think we should keep this?

Yes, because it's not important to read the header when the codec is in BLOCK 
mode.  IIUC the main difference between CONTINUOUS and BLOCK mode is that BLOCK 
mode will be used when processing splits and CONTINUOUS mode is used when we're 
simply trying to decompress the data in one big chunk (i.e.: no splits).  BLOCK 
mode always will scan for the start of the bz2 block, so it will automatically 
skip a bz2 file header while searching for the start of the first bz2 block 
from the specified start offset.

Given the splittable codec is always scanning for the block and doesn't really 
care what bytes are being skipped, I'm now thinking we can go back to a much 
simpler implementation.  I think the code can check if we're in BLOCK mode to 
know whether we are processing splits or not.  If we are in BLOCK mode we avoid 
advertising the byte position if start offset is zero just as the previous 
patches.  In BLOCK mode we should also skip to file offset HEADER_LEN + 
SUB_HEADER_LEN + 1 if the start position is >=0 and < HEADER_LEN + 
SUB_HEADER_LEN.  That will put us one byte past the start of the first bz2 
block, and BLOCK mode will automatically scan forward to the next block.  This 
proposal is very similar to what was implemented in patch 003.  I think we just 
need to make it only do the position adjustment if we're in BLOCK mode.

> BZip2 drops and duplicates records when input split size is small
> -----------------------------------------------------------------
>                 Key: HADOOP-15206
>                 URL: https://issues.apache.org/jira/browse/HADOOP-15206
>             Project: Hadoop Common
>          Issue Type: Bug
>    Affects Versions: 2.8.3, 3.0.0
>            Reporter: Aki Tanaka
>            Priority: Major
>         Attachments: HADOOP-15206-test.patch, HADOOP-15206.001.patch, 
> HADOOP-15206.002.patch, HADOOP-15206.003.patch, HADOOP-15206.004.patch
> BZip2 can drop and duplicate record when input split file is small. I 
> confirmed that this issue happens when the input split size is between 1byte 
> and 4bytes.
> I am seeing the following 2 problem behaviors.
> 1. Drop record:
> BZip2 skips the first record in the input file when the input split size is 
> small
> Set the split size to 3 and tested to load 100 records (0, 1, 2..99)
> {code:java}
> 2018-02-01 10:52:33,502 INFO  [Thread-17] mapred.TestTextInputFormat 
> (TestTextInputFormat.java:verifyPartitions(317)) - 
> splits[1]=file:/work/count-mismatch2/hadoop/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/target/test-dir/TestTextInputFormat/test.bz2:3+3
>  count=99{code}
> > The input format read only 99 records but not 100 records
> 2. Duplicate Record:
> 2 input splits has same BZip2 records when the input split size is small
> Set the split size to 1 and tested to load 100 records (0, 1, 2..99)
> {code:java}
> 2018-02-01 11:18:49,309 INFO [Thread-17] mapred.TestTextInputFormat 
> (TestTextInputFormat.java:verifyPartitions(318)) - splits[3]=file 
> /work/count-mismatch2/hadoop/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/target/test-dir/TestTextInputFormat/test.bz2:3+1
>  count=99
> 2018-02-01 11:18:49,310 WARN [Thread-17] mapred.TestTextInputFormat 
> (TestTextInputFormat.java:verifyPartitions(308)) - conflict with 1 in split 4 
> at position 8
> {code}
> I experienced this error when I execute Spark (SparkSQL) job under the 
> following conditions:
> * The file size of the input files are small (around 1KB)
> * Hadoop cluster has many slave nodes (able to launch many executor tasks)

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